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Linear building pattern recognition in topographical maps combining convex polygon decomposition / Zhiwei Wei in Geocarto international, vol 38 n° inconnu ([01/01/2023])
[article]
Titre : Linear building pattern recognition in topographical maps combining convex polygon decomposition Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Su Ding, Auteur ; Lu Cheng, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carte topographique
[Termes IGN] construction
[Termes IGN] décomposition
[Termes IGN] détection du bâti
[Termes IGN] forme linéaire
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] Ordnance Survey (UK)
[Termes IGN] polygone
[Termes IGN] reconnaissance de formesRésumé : (auteur) Building patterns are crucial for urban form understanding, automated map generalization, and 3 D city model visualization. The existing studies have recognized various building patterns based on visual perception rules in which buildings are considered as a whole. However, some visually aware patterns may fail to be recognized with these approaches because human vision is also proved as a part-based system. This paper first proposed an approach for linear building pattern recognition combining convex polygon decomposition. Linear building patterns including collinear patterns and curvilinear patterns are defined according to the proximity, similarity, and continuity between buildings. Linear building patterns are then recognized by combining convex polygon decomposition, in which a building can be decomposed into sub-buildings for pattern recognition. A novel node concavity is developed based on polygon skeletons which is applicable for building polygons with holes or not in the building decomposition. And building’s orthogonal features are also considered in the building decomposition. Two datasets collected from Ordnance Survey (OS) were used in the experiments to verify the effectiveness of the proposed approach. The results indicate that our approach achieves 25.57% higher precision and 32.23% higher recall in collinear pattern recognition and 15.67% higher precision and 18.52% higher recall in curvilinear pattern recognition when compared to existing approaches. Recognition of other kinds of building patterns including T-shaped and C-shaped patterns combining convex polygon decomposition are also discussed in this approach. Numéro de notice : A2022-263 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2055794 Date de publication en ligne : 27/03/2022 En ligne : https://doi.org/10.1080/10106049.2022.2055794 Format de la ressource électronique : 27/03/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100260
in Geocarto international > vol 38 n° inconnu [01/01/2023][article]A machine learning method for Arctic lakes detection in the permafrost areas of Siberia / Piotr Janiec in European journal of remote sensing, vol 56 n° 1 (2023)
[article]
Titre : A machine learning method for Arctic lakes detection in the permafrost areas of Siberia Type de document : Article/Communication Auteurs : Piotr Janiec, Auteur ; Jakub Nowosad, Auteur ; Sbigniew Zwoliński, Auteur Année de publication : 2023 Article en page(s) : n° 2163923 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] Arctique
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Landsat-8
[Termes IGN] lac glaciaire
[Termes IGN] MERIT
[Termes IGN] modèle numérique de surface
[Termes IGN] pergélisol
[Termes IGN] Short Waves InfraRed
[Termes IGN] SibérieRésumé : (auteur) Thermokarst lakes are the main components of the vast Arctic and subarctic landscapes. These lakes can serve as geoindicators of permafrost degradation; therefore, proper lake distribution assessment methods are necessary. In this study, we compared four machine learning methods to improve existing lake detection systems. The northern part of Yakutia was selected as the study area owing to its complex environment. We used data from Landsat 8 and spectral indices to take into account the spectral characteristics of the lakes, and MERIT DEM data to take into account the topography. The lowest accuracy was found for the classification and regression trees (CART) method (overall accuracy = 81%). On the other hand, the random forests (RF) classification provided the best results (overall accuracy = 92%), and only this classification coped well in all problematic areas, such as shaded and humid areas, near steep slopes, burn scars, and rivers. The altitude and bands SWIR1 (Short wave infrared 1), SWIR2 (Short wave infrared 2), and Green were the most important. Spectral indices did not have significant impact on the classification results in the specific conditions of the thermokarst lakes environment. 17,700 lakes were identified with the total area of 271.43 km2. Numéro de notice : A2023-218 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2022.2163923 Date de publication en ligne : 19/01/2023 En ligne : https://doi.org/10.1080/22797254.2022.2163923 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103156
in European journal of remote sensing > vol 56 n° 1 (2023) . - n° 2163923[article]Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)
[article]
Titre : Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami Type de document : Article/Communication Auteurs : Riantini Virtriana, Auteur ; Agung Budi Harto, Auteur ; Fiza Wira Atmaja, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 28 - 51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] base de données d'images
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] dommage matériel
[Termes IGN] données Copernicus
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Worldview
[Termes IGN] Indonésie
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation d'image
[Termes IGN] tsunamiRésumé : (auteur) In Indonesia, tsunamis are frequent events. In 2000–2016, there were 44 tsunami events in Indonesia, with financial losses reaching 43.38 trillion. In 2018, a tsunami occurred in the Sunda Strait due to the eruption of the Anak Krakatau Volcano, which caused many fatalities and much building damage. This study aimed to detect the building damage in the Labuan District, Banten Province. Machine learning methods were used to detect building damage using random forest with object-based techniques. No previous research has combined selected predictors into scenarios; hence, the novelty of this study is combining various random forest predictors to identify the extent of building damage using 14 predictor scenarios. In addition, field surveys were conducted two years and nine months after the tsunami to observe the changes and efforts made. The results of the random forest classification were validated and compared with three datasets, namely xBD, Copernicus, and field survey data. The results of this study can help classify the level of building damage using satellite imagery to improve mitigation in tsunami-prone areas. Numéro de notice : A2023-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/19475705.2022.2147455 Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1080/19475705.2022.2147455 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102307
in Geomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - pp 28 - 51[article]A method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination / Kaili Zhang in Geocarto international, vol 38 n° 1 ([01/01/2023])
[article]
Titre : A method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination Type de document : Article/Communication Auteurs : Kaili Zhang, Auteur ; Yonggang Chen, Auteur ; Wentao Wang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2158948 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spatiale
[Termes IGN] analyse spectrale
[Termes IGN] classification Spectral angle mapper
[Termes IGN] classification spectrale
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] données vectorielles
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] pixel
[Termes IGN] précision de la classification
[Termes IGN] signature texturale
[Termes IGN] similitude spectrale
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) In the study of remote sensing image classification, feature extraction and selection is an effective method to distinguish different classification targets. Constructing a high-quality spectral-spatial feature and feature combination has been a worthwhile topic for improving classification accuracy. In this context, this study constructed a spectral-spatial feature, namely the Pixel Neighbourhood Similarity (PNS) index. Meanwhile, the PNS index and 19 spectral, textural and terrain features were involved in the Correlation-based Feature Selection (CFS) algorithm for feature selection to generate a feature combination (PNS-CFS). To explore how PNS and PNS-CFS improve the classification accuracy of land types. The results show that: (1) The PNS index exhibited clear boundaries between different land types. The performance quality of PNS was relatively highest compared to other spectral-spatial features, namely the Vector Similarity (VS) index, the Change Vector Intensity (CVI) index and the Correlation (COR) index. (2) The Overall Accuracy (OA) of the PNS-CFS was 94.66% and 93.59% in study areas 1 and 2, respectively. These were 7.48% and 6.02% higher than the original image data (ORI) and 7.27% and 2.39% higher than the single-dimensional feature combination (SIN-CFS). Compared to the feature combinations of VS, CVI, and COR indices (VS-CFS, CVI-COM, COR-COM), PNS-CFS had the relatively highest performance and classification accuracy. The study demonstrated that the PNS index and PNS-CFS have a high potential for image classification. Numéro de notice : A2023-059 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2158948 Date de publication en ligne : 03/01/2023 En ligne : https://doi.org/10.1080/10106049.2022.2158948 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102397
in Geocarto international > vol 38 n° 1 [01/01/2023] . - n° 2158948[article]Mitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning / Roope Ruotsalainen in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)
[article]
Titre : Mitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning Type de document : Article/Communication Auteurs : Roope Ruotsalainen, Auteur ; Timo Pukkala, Auteur ; Veli-Pekka Ikonen, Auteur Année de publication : 2023 Article en page(s) : pp 121 - 134 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] altitude
[Termes IGN] canopée
[Termes IGN] dommage forestier causé par facteurs naturels
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] gestion forestière
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle numérique de surface
[Termes IGN] pondération
[Termes IGN] prévention des risques
[Termes IGN] topographie locale
[Termes IGN] vent
[Termes IGN] voisinage (relation topologique)
[Vedettes matières IGN] ForesterieRésumé : (auteur) Wind damage and the bark beetle outbreaks associated with it are major threats to non-declining, long-term wood production in boreal forests. We studied whether the risk of wind damage in a forested landscape could be decreased by using stand neighbourhood information in conjunction with terrain elevation information. A reference management plan minimized the differences in canopy height at stand boundaries and did not utilize information on the topography of the terrain, overlooking the possibility that the risk of windthrow may depend on the elevation of the terrain. Alternative management plans were developed by using four different weighting schemes when minimizing differences in canopy height at stand boundaries: (1) no weight (reference); (2) mean terrain elevation at the stand boundary; (3) deviation of the mean elevation of the boundary from the mean elevation of the terrain within a 100-m radius and (4) multipliers that described the effect of topography on wind speed at the stand boundary. For each management plan, we calculated the total number of at-risk trees and the total area of vulnerable stand edge. These statistics were based on the calculated critical wind speeds needed to uproot trees in stand edge zones. Minimization of the weighted mean of canopy height differences between adjacent stands resulted in homogeneous landscapes in terms of canopy height. Continuous cover management was often preferred instead of rotation management due to smaller canopy height differences between adjacent stands and its economical superiority. The best weighting scheme for calculating the mean canopy height difference between adjacent stands was the deviation between the mean elevation of the boundary and the mean elevation of the terrain within 100 m of the boundary. However, the differences between the weighting schemes were small. It was found that reasonably simple methods, based on a digital terrain model, a stand map, and the canopy heights of stands, could be used in forest planning to minimize the risk of wind damage. Validation against actual wind damages is required to assess the reliability of the results and to further develop the methodology presented. Numéro de notice : A2023-114 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpac039 Date de publication en ligne : 08/10/2022 En ligne : https://doi.org/10.1093/forestry/cpac039 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102481
in Forestry, an international journal of forest research > vol 96 n° 1 (January 2023) . - pp 121 - 134[article]PermalinkModeling the gravitational effects of ocean tide loading at coastal stations in the China earthquake gravity network based on GOTL software / Chuandong Zhu in Journal of applied geodesy, vol 17 n° 1 (January 2023)PermalinkModern vectorization and alignment of historical maps: An application to Paris Atlas (1789-1950) / Yizi Chen (2023)PermalinkMulti-information PointNet++ fusion method for DEM construction from airborne LiDAR data / Hong Hu in Geocarto international, vol 38 n° 1 ([01/01/2023])PermalinkA nonlinear Gauss-Helmert model and its robust solution for seafloor control point positioning / Yingcai Kuang in Marine geodesy, vol 46 n° 1 (January 2023)PermalinkParameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution / Estera Trzcina in Journal of geodesy, vol 97 n° 1 (January 2023)PermalinkPermalinkProduction of orthophoto map using mobile photogrammetry and comparative assessment of cost and accuracy with satellite imagery for corridor mapping: a case study in Manesar, Haryana, India / Manuj Dev in Annals of GIS, vol 29 n° 1 (January 2023)PermalinkPrototype-guided multitask adversarial network for cross-domain LiDAR point clouds semantic segmentation / Zhimin Yuan in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)PermalinkSemi-supervised label propagation for multi-source remote sensing image change detection / Fan Hao in Computers & geosciences, vol 170 (January 2023)PermalinkPermalinkPermalinkA survey and benchmark of automatic surface reconstruction from point clouds / Raphaël Sulzer (2023)PermalinkThe cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 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